The present invention relates in general to vehicle speed control systems, and, more specifically, to optimizing energy efficiency of a speed-controlled vehicle without advance knowledge of actual road grade variations along a route of travel or any pre-planning of a route.
Vehicle manufacturers continually strive to minimize energy consumption for driving a vehicle (e.g., maximizing the distance driven per unit of gas for a gasoline vehicle or unit of electrical charge for an electrically-driven vehicle). Important influences on efficiency include the speed at which the vehicle is driven, road grade variations over the driven route, and traffic conditions. Automatic speed control (i.e., cruise control) systems can have a beneficial impact on fuel economy by reducing the amount of time spent accelerating the vehicle, especially during highway driving. Maintaining a single speed setting during uphill and downhill road grades, however, consumes more fuel than if the vehicle is allowed to vary in order to take advantage of road grade variations to optimize fuel consumption. If upcoming changes in road grade are known in advance (such as from GPS-based maps and advance routing), then temporary offsets can be introduced in the speed setting that accordingly improve energy consumption. However, GPS navigation devices and the necessary in-vehicle map data, computational, and/or remote data communications requirements to determine such offsets in real-time on-board a vehicle represent a significant cost or may be unavailable in some areas. Therefore, it would be desirable to lessen such requirements for determining appropriate speed offsets.
The paper Kolmanovsky et al., Terrain and Traffic Optimized Vehicle Speed Control, 6TH IFAC SYMPOSIUM ADVANCES IN AUTOMOTIVE CONTROL, MUNICH, JULY 2010, which is incorporated herein by reference, describes the derivation of a control policy for use by a vehicle in a specific geographic region for best on-average performance without advance knowledge of a route to be traveled or the actual upcoming road grade being approached. The control policy prescribes a vehicle speed setpoint to achieve optimal tradeoff between expected average fuel economy and expected average travel speed. Terrain and traffic properties (i.e., driving conditions) are aggregated as transition probability matrices (TPM) of a Markov Chain model. Stochastic dynamic programming generates the control policy off-line (i.e., off-board the vehicle during the design phase of the vehicle using independent characterization of the terrain) based on a value function which is included as a terminal cost in the optimization of another cost function that reflects predicted fuel consumption and speed. The resulting control policy is then loaded into the vehicle for use when it is driven in the corresponding region.
The paper McDonough et al., Modeling of Vehicle Driving Conditions Using Transition Probability Models, 2011 IEEE MULTI-CONFERENCE ON CONTROL APPLICATIONS, DENVER, SEPTEMBER 2011, which is incorporated herein by reference, discloses the use of Kullback-Leibeler (KL) divergence between transition probability matrices to differentiate between similar or dissimilar driving conditions. Based on a TPM corresponding to a vehicle's current driving conditions, KL divergence could be used to interpolate control policies developed for a discrete set of typical driving cycles for adaptation of vehicle powertrain operation to the terrain and traffic conditions.
The proposed systems depend heavily upon a priori data collection (to characterize various regions and driving conditions) and analysis (to create the control policies to be pre-loaded into target vehicles). A more practical approach is needed for deploying optimizations across a large and diverse fleet of vehicle models being driven over a diverse set of regions and driving conditions. Furthermore, it would be desirable to increase the robustness of the optimization by better selection of the properties that are used to characterize the underlying driving conditions.